Human alveolar T lymphocyte responses to Mycobacterium tuberculosis antigens: role for CD4+ and CD8+ cytotoxic T cells and relative resistance of alveolar macrophages to lysis.

T cell-mediated cytotoxicity against Mycobacterium tuberculosis (MTB)-infected macrophages may be a major mechanism of specific host defense, but little is known about such activities in the lung. Thus, the capacity of alveolar lymphocyte MTB-specific cell lines (AL) and alveolar macrophages (AM) from tuberculin skin test-positive healthy subjects to serve as CTL and target cells, respectively, in response to MTB (H37Ra) or purified protein derivative (PPD) was investigated. Mycobacterial Ag-pulsed AM were targets of blood CTL activity at E:T ratios of > or = 30:1 (51Cr release assay), but were significantly more resistant to cytotoxicity than autologous blood monocytes. PPD- plus IL-2-expanded AL and blood lymphocytes were cytotoxic for autologous mycobacterium-stimulated monocytes at E:T ratios of > or = 10:1. The CTL activity of lymphocytes expanded with PPD was predominantly class II MHC restricted, whereas the CTL activity of lymphocytes expanded with PPD plus IL-2 was both class I and class II MHC restricted. Both CD4+ and CD8+ T cells were enriched in BL and AL expanded with PPD and IL-2, and both subsets had mycobacterium-specific CTL activity. Such novel cytotoxic responses by CD4+ and CD8+ T cells may be a major mechanism of defense against MTB at the site of disease activity.

Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity

Predicting clinical response to anticancer drugs remains a major challenge in cancer treatment. Emerging reports indicate that the tumour microenvironment and heterogeneity can limit the predictive power of current biomarker-guided strategies for chemotherapy. Here we report the engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumour microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum. The functional response of tumour ecosystems, engineered from 109 patients, to anticancer drugs, together with the corresponding clinical outcomes, is used to train a machine learning algorithm; the learned model is then applied to predict the clinical response in an independent validation group of 55 patients, where we achieve 100% sensitivity in predictions while keeping specificity in a desired high range. The tumour ecosystem and algorithm, together termed the CANScript technology, can emerge as a powerful platform for enabling personalized medicine.